_base_ = "../_base_/default_runtime.py"

# model settings
img_size = 550
model = dict(
    type="YOLACT",
    pretrained="torchvision://resnet50",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,  # do not freeze stem
        norm_cfg=dict(type="BN", requires_grad=True),
        norm_eval=False,  # update the statistics of bn
        zero_init_residual=False,
        style="pytorch",
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs="on_input",
        num_outs=5,
        upsample_cfg=dict(mode="bilinear"),
    ),
    bbox_head=dict(
        type="YOLACTHead",
        num_classes=80,
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type="AnchorGenerator",
            octave_base_scale=3,
            scales_per_octave=1,
            base_sizes=[8, 16, 32, 64, 128],
            ratios=[0.5, 1.0, 2.0],
            strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
            centers=[(550 * 0.5 / x, 550 * 0.5 / x) for x in [69, 35, 18, 9, 5]],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.1, 0.1, 0.2, 0.2],
        ),
        loss_cls=dict(
            type="CrossEntropyLoss",
            use_sigmoid=False,
            reduction="none",
            loss_weight=1.0,
        ),
        loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.5),
        num_head_convs=1,
        num_protos=32,
        use_ohem=True,
    ),
    mask_head=dict(
        type="YOLACTProtonet",
        in_channels=256,
        num_protos=32,
        num_classes=80,
        max_masks_to_train=100,
        loss_mask_weight=6.125,
    ),
    segm_head=dict(
        type="YOLACTSegmHead",
        num_classes=80,
        in_channels=256,
        loss_segm=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
    ),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type="MaxIoUAssigner",
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0.0,
            ignore_iof_thr=-1,
            gt_max_assign_all=False,
        ),
        # smoothl1_beta=1.,
        allowed_border=-1,
        pos_weight=-1,
        neg_pos_ratio=3,
        debug=False,
    ),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        iou_thr=0.5,
        top_k=200,
        max_per_img=100,
    ),
)
# dataset settings
dataset_type = "CocoDataset"
data_root = "data/coco/"
img_norm_cfg = dict(
    mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile", to_float32=True),
    dict(type="LoadAnnotations", with_bbox=True, with_mask=True),
    dict(type="FilterAnnotations", min_gt_bbox_wh=(4.0, 4.0)),
    dict(
        type="PhotoMetricDistortion",
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18,
    ),
    dict(
        type="Expand",
        mean=img_norm_cfg["mean"],
        to_rgb=img_norm_cfg["to_rgb"],
        ratio_range=(1, 4),
    ),
    dict(
        type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3
    ),
    dict(type="Resize", img_scale=(img_size, img_size), keep_ratio=False),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels", "gt_masks"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(img_size, img_size),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=False),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_train2017.json",
        img_prefix=data_root + "train2017/",
        pipeline=train_pipeline,
    ),
    val=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_val2017.json",
        img_prefix=data_root + "val2017/",
        pipeline=test_pipeline,
    ),
    test=dict(
        type=dataset_type,
        ann_file=data_root + "annotations/instances_val2017.json",
        img_prefix=data_root + "val2017/",
        pipeline=test_pipeline,
    ),
)
# optimizer
optimizer = dict(type="SGD", lr=1e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict()
# learning policy
lr_config = dict(
    policy="step",
    warmup="linear",
    warmup_iters=500,
    warmup_ratio=0.1,
    step=[20, 42, 49, 52],
)
runner = dict(type="EpochBasedRunner", max_epochs=55)
cudnn_benchmark = True
evaluation = dict(metric=["bbox", "segm"])
